Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

698
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
698
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

403
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
403
pH Scale02:41

pH Scale

79.2K
Hydronium and hydroxide ions are present both in pure water and in all aqueous solutions, and their concentrations are inversely proportional as determined by the ion product of water (Kw). The concentrations of these ions in a solution are often critical determinants of the solution’s properties and the chemical behaviors of its other solutes. Two different solutions can differ in their hydronium or hydroxide ion concentrations by a million, billion, or even trillion times. A common means of...
79.2K
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

1.4K
Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
1.4K
Performing a Simple Data Analysis using MS-Excel Function01:17

Performing a Simple Data Analysis using MS-Excel Function

937
Microsoft Excel offers a suite of functions and tools ideal for statistical analysis, making it accessible to students and researchers. This article outlines fundamental Excel functions pivotal for data analysis.
SUM: This function calculates the total sum of a range of values. It's the foundation for aggregating data, essential for determining overall trends and totals in datasets.
AVERAGE: It computes the mean value of a given set of numbers, providing a quick insight into the central...
937

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Elevated IL-6 receptor expression on CD4+ T cells contributes to the increased Th17 responses in patients with chronic hepatitis B.

Virology journal·2011
Same author

Neurochemical plasticity of nitric oxide synthase isoforms in neurogenic detrusor overactivity after spinal cord injury.

Neurochemical research·2011
Same author

[Clinical significance of 5-HT and DA levels in serum and cerebrospinal fluid of the patients with delayed encephalopathy after acute carbon monoxide poisoning].

Zhonghua lao dong wei sheng zhi ye bing za zhi = Zhonghua laodong weisheng zhiyebing zazhi = Chinese journal of industrial hygiene and occupational diseases·2011
Same author

Reconstitution of lysosomal NAADP-TRP-ML1 signaling pathway and its function in TRP-ML1(-/-) cells.

American journal of physiology. Cell physiology·2011
Same author

[The association between HBV genotyping and clinical characteristics and expression of TH1/TH2 cytokines].

Zhonghua shi yan he lin chuang bing du xue za zhi = Zhonghua shiyan he linchuang bingduxue zazhi = Chinese journal of experimental and clinical virology·2011
Same author

Bis[5-(2-pyrid-yl)pyrazine-2-carbonitrile]-silver(I) tetra-fluorido-borate.

Acta crystallographica. Section E, Structure reports online·2011

Related Experiment Video

Updated: Jan 27, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.2K

WaveICA: A novel algorithm to remove batch effects for large-scale untargeted metabolomics data based on wavelet

Kui Deng1, Fan Zhang2, Qilong Tan1

  • 1Department of Epidemiology and Biostatistics, School of Public Health, Harbin Medical University, Harbin, 150086, China.

Analytica Chimica Acta
|March 31, 2019
PubMed
Summary
This summary is machine-generated.

WaveICA, a novel algorithm, effectively removes batch effects in large-scale untargeted metabolomics studies. This method improves data quality and enhances biomarker discovery by cleaning liquid chromatography-mass spectrometry data.

Keywords:
Batch effectData normalizationIndependent component analysisMetabolomicsWavelet transform

More Related Videos

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.7K

Related Experiment Videos

Last Updated: Jan 27, 2026

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome
05:35

An Integrated Workflow of Identification and Quantification on FDR Control-Based Untargeted Metabolome

Published on: September 20, 2022

4.2K
Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain
07:10

Untargeted Liquid Chromatography-Mass Spectrometry-Based Metabolomics Analysis of Wheat Grain

Published on: March 13, 2020

10.6K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

21.7K

Area of Science:

  • Metabolomics
  • Biomarker Discovery
  • Computational Biology

Background:

  • Large-scale untargeted metabolomics studies are crucial for understanding disease pathogenesis and identifying biomarkers.
  • Liquid chromatography-mass spectrometry (LC-MS) is a common platform for analyzing metabolomics samples in multiple batches.
  • Batch effects, arising from non-biological systematic biases, are inherent in large-scale studies and can lead to misleading statistical analyses if not managed.

Purpose of the Study:

  • To introduce WaveICA, a novel algorithm designed to capture and remove batch effects from large-scale metabolomics data.
  • To improve the accuracy and reliability of statistical analyses in untargeted metabolomics studies.
  • To enhance the biological insights and biomarker discovery potential of metabolomics data.

Main Methods:

  • WaveICA utilizes a combination of wavelet transform and independent component analysis (ICA) for threshold processing.
  • The algorithm analyzes the time trend of samples based on injection order.
  • It decomposes data into multi-scale components to extract and eliminate batch effect information, yielding clean data.

Main Results:

  • Application of WaveICA resulted in significant clustering of quality control samples (QCS) and subject samples in PCA score plots.
  • The average Pearson correlation coefficients for QCS peaks increased from 0.872 to 0.972, indicating improved data consistency.
  • WaveICA demonstrated superior performance compared to three other representative methods in improving classification accuracy for metabolomics data.

Conclusions:

  • WaveICA is an efficient algorithm for removing batch effects in large-scale untargeted metabolomics data.
  • The method preserves and reveals more biological information, enhancing the utility of metabolomics studies.
  • WaveICA serves as a valuable preprocessing tool for raw metabolomics data in large-scale research endeavors.